1. Field of the Invention
The invention generally relates to data communications devices such as WLAN (Wireless Local Area Network) receivers and corresponding methods, and in particular to channel estimation techniques.
2. Description of the Related Art
In data communications systems, channels provide the connection between the transmitters and the receivers. Dependent on the physical media used, different types of channels can be distinguished. For instance, if the physical channel is a pair of wires that carry the electrical signal, the channel is a wire line channel. Other examples of physical channels are wireless electromagnetic channels and fiber-optic channels.
An example of a communications system using wireless channels is the WLAN system which is based on the 802.11b standard. A WLAN system is a flexible data communications system that uses radio frequency (RF) or infrared technology for transmitting and receiving data over the air, thereby minimizing the need for wired connections. Most WLAN systems use spread spectrum technology, a wide-band radio frequency technique developed for use in reliable and secure communications systems. Two types of spread spectrum radio systems are frequently used: frequency hopping and direct sequence systems.
In data communications systems such as WLAN systems it is often advantageous to perform a channel estimation to determine one or more channel coefficients that are indicative of channel properties. Some of the channel estimation techniques are LMS (Least Mean Square) based. The LMS algorithm is a technique that uses a stochastic gradient algorithm which in turn generally optimizes a function F with respect to some set of complex parameters a*:=(al, . . . , am). An update of the parameters at iteration k can be determined by
                    a        _            *        ⁡          (              k        +        1            )        =                              a          _                *            ⁡              (        k        )              -          δ      ·                        ∂          F                          ∂                                    a              _                        ⁡                          (              k              )                                          where δ is the step size (or learning rate). Usually, F=|ε|2 where |ε|2 is a quadratic expression of an error. The step size is the constant that specifies how much the gradient information is scaled to correct the previous coefficients. Thus, the step size determines the speed of convergence and the stability of the algorithm. If the step size δ is for instance very small, then the coefficients are not altered by a significant amount at each update. With a large step size, more gradient information is included in each update. However, when the step size is too large the coefficients may be changed too much and there will be no convergence anymore.
When performing channel estimation algorithms, in particular those which are based on the LMS technique, it has been found that the circuits used for this purpose are required to be of significant complexity so that channel estimation circuits are usually highly involved and lead to substantial circuit development and manufacturing costs.